Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "238" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 32 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 32 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460015 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.147755 | -0.435440 | 0.613040 | 0.370739 | -0.469022 | -0.810803 | -1.988998 | -2.229859 | 0.5709 | 0.5715 | 0.3537 | nan | nan |
| 2460014 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.773786 | -0.425007 | 0.353142 | 0.006469 | -1.110626 | -0.692184 | -1.583610 | -1.804616 | 0.5426 | 0.5545 | 0.3565 | nan | nan |
| 2460013 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.155222 | -0.303000 | 0.645125 | 0.528083 | -0.561461 | -0.873862 | -1.935880 | -2.117248 | 0.5625 | 0.5729 | 0.3605 | nan | nan |
| 2460012 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.187563 | -0.388490 | 0.511896 | 0.378538 | -0.524457 | -0.986101 | -2.498373 | -2.680520 | 0.5638 | 0.5740 | 0.3538 | nan | nan |
| 2460011 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.095880 | -0.286097 | 0.929314 | 0.685380 | -0.119448 | -1.194463 | -1.599519 | -1.992138 | 0.5843 | 0.5917 | 0.3504 | nan | nan |
| 2460010 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.196099 | -0.115052 | 1.230646 | 0.626947 | 0.104941 | -0.766423 | -1.665664 | -1.764158 | 0.5990 | 0.6072 | 0.3561 | nan | nan |
| 2460009 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.297234 | -0.595703 | 0.991177 | 0.573650 | -0.140647 | -0.548026 | -2.011619 | -1.707166 | 0.6017 | 0.6116 | 0.3590 | nan | nan |
| 2460008 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.132082 | -0.048882 | 1.148449 | 0.492289 | 0.105533 | -0.603181 | -0.267290 | -0.817730 | 0.6360 | 0.6458 | 0.3281 | nan | nan |
| 2460007 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.254948 | -0.202403 | 1.127378 | 0.574746 | 0.022779 | -0.580966 | -1.930258 | -1.976724 | 0.6011 | 0.6149 | 0.3444 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5667 | 0.5897 | 0.3366 | nan | nan |
| 2459998 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.106735 | -0.453821 | 0.718666 | 0.336261 | -0.673851 | -0.345830 | -1.640473 | -1.673481 | 0.5871 | 0.6038 | 0.3830 | nan | nan |
| 2459997 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.004218 | -0.516278 | 0.999820 | 0.473127 | -0.260469 | -0.794309 | -2.139007 | -2.453630 | 0.5984 | 0.6168 | 0.3881 | nan | nan |
| 2459996 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.354859 | -0.514762 | 0.935944 | 0.695687 | -0.111154 | -0.565927 | -0.964107 | -1.311861 | 0.6097 | 0.6245 | 0.3978 | nan | nan |
| 2459995 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.120309 | -0.647007 | 0.957370 | 0.526035 | -0.465710 | -0.351116 | -1.187498 | -1.508797 | 0.6010 | 0.6177 | 0.3858 | nan | nan |
| 2459994 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.086389 | -0.535189 | 0.903191 | 0.266146 | -0.392966 | -0.939831 | -1.246499 | -1.434619 | 0.5960 | 0.6117 | 0.3807 | nan | nan |
| 2459993 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 240.321593 | 240.004359 | inf | inf | 2768.406276 | 2574.959540 | 5254.013025 | 4855.969777 | nan | nan | nan | nan | nan |
| 2459991 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.043800 | -0.403554 | 1.076067 | 0.423618 | -0.224503 | -0.759496 | -1.360662 | -1.532191 | 0.5999 | 0.6065 | 0.3884 | nan | nan |
| 2459990 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.091460 | -0.144195 | 1.058021 | 0.465771 | -0.318139 | -0.634209 | -1.625611 | -1.952137 | 0.5979 | 0.6085 | 0.3869 | nan | nan |
| 2459989 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.257148 | -0.170015 | 1.151194 | 0.369585 | -0.469433 | -0.759292 | -1.249059 | -1.488505 | 0.5932 | 0.6092 | 0.3879 | nan | nan |
| 2459988 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.467128 | -0.322953 | 1.526963 | 0.991799 | 0.294941 | -0.613638 | -1.513185 | -1.619846 | 0.5944 | 0.6099 | 0.3804 | nan | nan |
| 2459987 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.251961 | -0.595439 | 0.938088 | 0.282838 | -0.647501 | -0.814822 | -1.815446 | -1.891592 | 0.6026 | 0.6150 | 0.3784 | nan | nan |
| 2459986 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.063959 | -0.411287 | 1.029063 | 0.486705 | -0.375459 | -0.721252 | -0.547925 | -0.950014 | 0.6160 | 0.6339 | 0.3506 | nan | nan |
| 2459985 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.546223 | -0.598224 | 1.292284 | 0.771967 | 0.034529 | -1.061546 | -2.237545 | -2.509523 | 0.6023 | 0.6155 | 0.3855 | nan | nan |
| 2459984 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.014408 | -0.617758 | 0.960035 | 0.499233 | 0.127176 | -0.426640 | -0.819017 | -0.970808 | 0.6195 | 0.6343 | 0.3645 | nan | nan |
| 2459983 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.265297 | -0.315075 | 0.944831 | 0.465245 | -0.633809 | -1.056534 | -1.085035 | -1.245306 | 0.6278 | 0.6486 | 0.3317 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.302771 | 0.671399 | 0.678663 | 0.203489 | -1.040829 | -0.987119 | -0.629666 | -0.946733 | 0.6730 | 0.6722 | 0.3036 | nan | nan |
| 2459981 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.403267 | -0.051138 | 1.161181 | 0.608121 | -0.396224 | -1.082356 | -1.624804 | -1.728398 | 0.6065 | 0.6172 | 0.3836 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.181181 | -0.329189 | 1.156805 | 0.537788 | -0.463448 | -1.432545 | 0.159912 | -0.628738 | 0.6405 | 0.6471 | 0.3203 | nan | nan |
| 2459979 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.589768 | -0.019929 | 0.791927 | 0.046276 | -0.581253 | -0.996178 | -1.589682 | -1.770071 | 0.5977 | 0.6119 | 0.3846 | nan | nan |
| 2459978 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.424543 | -0.038572 | 0.994347 | 0.299839 | -0.580558 | -1.036199 | -1.841134 | -1.916521 | 0.5987 | 0.6112 | 0.3918 | nan | nan |
| 2459977 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.097191 | -0.137254 | 0.788944 | 0.148569 | -0.592538 | -1.458066 | -1.923545 | -2.025480 | 0.5621 | 0.5744 | 0.3500 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.327912 | -0.090130 | 0.872937 | 0.268134 | -0.689177 | -0.423338 | -1.352683 | -1.529746 | 0.6056 | 0.6175 | 0.3834 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.613040 | -0.435440 | -0.147755 | 0.370739 | 0.613040 | -0.810803 | -0.469022 | -2.229859 | -1.988998 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.353142 | -0.773786 | -0.425007 | 0.353142 | 0.006469 | -1.110626 | -0.692184 | -1.583610 | -1.804616 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.645125 | -0.155222 | -0.303000 | 0.645125 | 0.528083 | -0.561461 | -0.873862 | -1.935880 | -2.117248 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.511896 | -0.187563 | -0.388490 | 0.511896 | 0.378538 | -0.524457 | -0.986101 | -2.498373 | -2.680520 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.929314 | 0.095880 | -0.286097 | 0.929314 | 0.685380 | -0.119448 | -1.194463 | -1.599519 | -1.992138 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.230646 | 0.196099 | -0.115052 | 1.230646 | 0.626947 | 0.104941 | -0.766423 | -1.665664 | -1.764158 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.991177 | 0.297234 | -0.595703 | 0.991177 | 0.573650 | -0.140647 | -0.548026 | -2.011619 | -1.707166 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.148449 | -0.048882 | 0.132082 | 0.492289 | 1.148449 | -0.603181 | 0.105533 | -0.817730 | -0.267290 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.127378 | 0.254948 | -0.202403 | 1.127378 | 0.574746 | 0.022779 | -0.580966 | -1.930258 | -1.976724 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.718666 | -0.106735 | -0.453821 | 0.718666 | 0.336261 | -0.673851 | -0.345830 | -1.640473 | -1.673481 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.999820 | -0.004218 | -0.516278 | 0.999820 | 0.473127 | -0.260469 | -0.794309 | -2.139007 | -2.453630 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.935944 | 0.354859 | -0.514762 | 0.935944 | 0.695687 | -0.111154 | -0.565927 | -0.964107 | -1.311861 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.957370 | 0.120309 | -0.647007 | 0.957370 | 0.526035 | -0.465710 | -0.351116 | -1.187498 | -1.508797 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.903191 | -0.086389 | -0.535189 | 0.903191 | 0.266146 | -0.392966 | -0.939831 | -1.246499 | -1.434619 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | inf | 240.321593 | 240.004359 | inf | inf | 2768.406276 | 2574.959540 | 5254.013025 | 4855.969777 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.076067 | -0.043800 | -0.403554 | 1.076067 | 0.423618 | -0.224503 | -0.759496 | -1.360662 | -1.532191 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.058021 | -0.144195 | -0.091460 | 0.465771 | 1.058021 | -0.634209 | -0.318139 | -1.952137 | -1.625611 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.151194 | -0.170015 | -0.257148 | 0.369585 | 1.151194 | -0.759292 | -0.469433 | -1.488505 | -1.249059 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.526963 | -0.322953 | 0.467128 | 0.991799 | 1.526963 | -0.613638 | 0.294941 | -1.619846 | -1.513185 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.938088 | -0.251961 | -0.595439 | 0.938088 | 0.282838 | -0.647501 | -0.814822 | -1.815446 | -1.891592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.029063 | -0.411287 | -0.063959 | 0.486705 | 1.029063 | -0.721252 | -0.375459 | -0.950014 | -0.547925 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.292284 | -0.598224 | 0.546223 | 0.771967 | 1.292284 | -1.061546 | 0.034529 | -2.509523 | -2.237545 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.960035 | 0.014408 | -0.617758 | 0.960035 | 0.499233 | 0.127176 | -0.426640 | -0.819017 | -0.970808 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.944831 | -0.265297 | -0.315075 | 0.944831 | 0.465245 | -0.633809 | -1.056534 | -1.085035 | -1.245306 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.678663 | -0.302771 | 0.671399 | 0.678663 | 0.203489 | -1.040829 | -0.987119 | -0.629666 | -0.946733 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.161181 | -0.051138 | -0.403267 | 0.608121 | 1.161181 | -1.082356 | -0.396224 | -1.728398 | -1.624804 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 1.156805 | -0.329189 | -0.181181 | 0.537788 | 1.156805 | -1.432545 | -0.463448 | -0.628738 | 0.159912 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.791927 | -0.589768 | -0.019929 | 0.791927 | 0.046276 | -0.581253 | -0.996178 | -1.589682 | -1.770071 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.994347 | -0.038572 | -0.424543 | 0.299839 | 0.994347 | -1.036199 | -0.580558 | -1.916521 | -1.841134 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.788944 | -0.097191 | -0.137254 | 0.788944 | 0.148569 | -0.592538 | -1.458066 | -1.923545 | -2.025480 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 238 | N18 | RF_ok | ee Power | 0.872937 | -0.090130 | -0.327912 | 0.268134 | 0.872937 | -0.423338 | -0.689177 | -1.529746 | -1.352683 |